With the rapid development of autonomous driving technologies, to ensure safe driving, driverless vehicles need to implement various functional requirements in real time, for example, positioning, traffic light recognition, route planning, overtaking, avoiding, and so on. However, in order to process various computation requests in real time, the driverless vehicle cannot rely only on a CPU to implement computation, but needs to further use hardware modules such as an FPGA and a GPU to accelerate the algorithm. Current solutions using the FPGA to accelerate the computation of the algorithm require the FPGA to operate at full speed.
However, the FPGA board needs to use all the resources when executing the computing operation at full speed. As a result, the FPGA board generally works in the state with the highest speed and the highest power consumption, leading to power consumption and hardware heat-dissipation problems. Therefore, it is necessary to reduce power consumption of the FPGA board while utilizing the computing power of the FPGA board.